Enzyme improvement in the absence of structural knowledge: A novel statistical approach

Yoram Barak; Yuval Nov; David F. Ackerley; A. Matin

doi:10.1038/ismej.2007.100

Enzyme improvement in the absence of structural knowledge: A novel statistical approach

Yoram Barak, Yuval Nov, David F. Ackerley, A. Matin

Department of Statistics

Research output: Contribution to journal › Article › peer-review

Abstract

Most existing methods for improving protein activity are laborious and costly, as they either require knowledge of protein structure or involve expression and screening of a vast number of protein mutants. We describe here a successful first application of a novel approach, which requires no structural knowledge and is shown to significantly reduce the number of mutants that need to be screened. In the first phase of this study, around 7000 mutants were screened through standard directed evolution, yielding a 230-fold improvement in activity relative to the wild type. Using sequence analysis and site-directed mutagenesis, an additional single mutant was then produced, with 500-fold improved activity. In the second phase, a novel statistical method for protein improvement was used; building on data from the first phase, only 11 targeted additional mutants were produced through site-directed mutagenesis, and the best among them achieved a >1500-fold improvement in activity over the wild type. Thus, the statistical model underlying the experiment was validated, and its predictions were shown to reduce laboratory labor and resources.

Original language	English
Pages (from-to)	171-179
Number of pages	9
Journal	ISME Journal
Volume	2
Issue number	2
DOIs	https://doi.org/10.1038/ismej.2007.100
State	Published - Feb 2008

Bibliographical note

Funding Information:
We are grateful to Drs Bruno Salles, Mike Benoit and Ms Mimi Keyhan for their useful advice and stimulating discussion. We thank Dr Stephen H Thorne for kindly supplying us with freshly made JC breast cancer cells. We also thank three anonymous referees whose insightful comments and suggestions greatly improved this article. This work was supported by Grants DE-FG03-97ER-624940 and DE-FG02-96ER20228 from the Natural and Accelerated Bioremediation Program of US Department of Energy, and Stanford Office of Technology Licensing (1105626-100-WOAAA). YB and DFA were supported, in part, by a Postdoctoral Fellowship from Lady Davis Postdoctoral Fellowship and FRST New Zealand (STAX0101) Fellowship, respectively.

Keywords

Directed evolution
Nov-Wein model
Protein design
Rational design

ASJC Scopus subject areas

Microbiology
Ecology, Evolution, Behavior and Systematics

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10.1038/ismej.2007.100

Cite this

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title = "Enzyme improvement in the absence of structural knowledge: A novel statistical approach",

abstract = "Most existing methods for improving protein activity are laborious and costly, as they either require knowledge of protein structure or involve expression and screening of a vast number of protein mutants. We describe here a successful first application of a novel approach, which requires no structural knowledge and is shown to significantly reduce the number of mutants that need to be screened. In the first phase of this study, around 7000 mutants were screened through standard directed evolution, yielding a 230-fold improvement in activity relative to the wild type. Using sequence analysis and site-directed mutagenesis, an additional single mutant was then produced, with 500-fold improved activity. In the second phase, a novel statistical method for protein improvement was used; building on data from the first phase, only 11 targeted additional mutants were produced through site-directed mutagenesis, and the best among them achieved a >1500-fold improvement in activity over the wild type. Thus, the statistical model underlying the experiment was validated, and its predictions were shown to reduce laboratory labor and resources.",

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Enzyme improvement in the absence of structural knowledge: A novel statistical approach

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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